Assumption-lean falsification tests of rate double-robustness of double-machine-learning estimators.

ABSTRACT: The class of doubly robust (DR) functionals studied by Rotnitzky et al. (2021) is of central importance in economics and biostatistics. It strictly includes both (i) the class of mean-square continuous functionals that can be written as an expectation of an affine functional of a conditional expectation studied by Chernozhukov et al. (2022b) and the class of functionals studied by Robins et al. (2008). The present state-of-the-art estimators for DR functionals ψ are double-machine-learning (DML) estimators (Chernozhukov et al., 2018). A DML estimator ψ^1 of ψ depends on estimates p^(x) and b^x of a pair of nuisance functions p(x) and bx, and is said to satisfy "rate double-robustness" if the Cauchy-Schwarz upper bound of its bias is o(n-1/2). Were it achievable, our scientific goal would h

SUBMITTER: Liu L 

PROVIDER: S-EPMC11052545 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature